2. Methodology
3.2 Quinoa expansion in Peru and its implications in land-use
of quinoa, which at the same time, is an example of the recovery of a neglected and underutilized species (NUS). Thus, it seems both timely and necessary to describe and analyze the changes in land-use that have accompanied the expansion of quinoa farming.
Below, there is an examination of the impact of the quinoa boom in relation to the trends in the use of agricultural land in Peru and their implications.
Fig. 4. The observed expansion of the acreage of quinoa vs. the predicted expansion in the traditional producing regions (TPRs) of Peru (1995-2014). The blue lines represent the observed expansion rate (OER) and the red lines represent the predicted expansion rate (PER) of the acreage of quinoa. The PER represents an estimate of what the expansion in the acreage of quinoa would have been as from 2008-2009, if there had been no quinoa boom in 2008-2009. The regions considered concentrate the largest part of the area occupied by quinoa in the country. * Production volume a > from 30000 MT; b 10000- 30000 MT; c 3000-10000 MT; d 1000-3000 MT; e < from 1000 MT.
TPR (aggregate) OER = 14.7%;
PER = 2.8%
Punoa OER = 4.4%;
PER = 3.3%
Arequipaa OER = 140.5%;
PER = 3.4%
Juninb OER = 46.4%;
PER = -28.6%
Ayacuchob OER = 38.7%;
PER = 4.3%
La Libertadc OER = 58.6%;
PER = 2.5%
Ancashc OER = 108.0%;
PER = -5.2%
Cuzcoc OER = 5.6%;
PER = 3.0%
Apurimacc OER = 16.9%;
PER = 1.5%
Huanucod OER = 41.9%;
PER = 0.9%
Huancavelicae OER = 13.1%;
PER = -8.9%
Cajamarcae OER = 17.6%;
PER = 1.8%
Displacement is related to the migration of activities from one place to another in a manner that brings about land-use changes in new locations. The second phenomenon,
Line Plot of multiple v ariables Harv ested Area in Prev isão quinoa 24v *20c
1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 10000
20000 30000 40000 50000 60000 70000
Line Plot of multiple variables Harvested Area in Previsão quinoa.stw 20v*20c
1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 10000
12000 14000 16000 18000 20000 22000 24000 26000 28000 30000 32000 34000
Line Plot of multiple variables Harvested Area in Previsão quinoa.stw 20v*20c
1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 -1000
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
Line Plot of multiple variables Harvested Area in Previsão quinoa.stw 20v*20c
1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 -1000
0 1000 2000 3000 4000 5000 6000
Line Plot of multiple variables Harvested Area in Previsão quinoa.stw 22v*20c
1995199719992001200320052007200920112013
0 1000 2000 3000 4000 5000 6000 7000 8000
Line Plot of multiple variables Harvested Area in Previsão quinoa.stw 20v*20c
1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 200
400 600 800 1000 1200 1400 1600 1800 2000 2200 2400
Line Plot of multiple variables Harvested Area in Previsão quinoa.stw 20v*20c
1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 0
200 400 600 800 1000 1200 1400 1600 1800
Line Plot of multiple variables Harvested Area in Previsão quinoa.stw 20v*20c
1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 0
500 1000 1500 2000 2500 3000 3500
Line Plot of multiple variables Harvested Area in Previsão quinoa.stw 20v*20c
1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 400
600 800 1000 1200 1400 1600 1800 2000 2200 2400
Line Plot of multiple variables Harvested Area in Previsão quinoa.stw 20v*20c
1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 200
400 600 800 1000 1200 1400
Line Plot of multiple variables Harvested Area in Previsão quinoa.stw 20v*20c
1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 0
100 200 300 400 500 600 700 800 900
Line Plot of multiple variables Harvested Area in Previsão quinoa.stw 20v*20c
1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 50
100 150 200 250 300 350 400 450
rebound, relates land-use changes with the measures taken to increase the efficiency of production, whether by the use of technology or an increase in the number of companies.
Finally, the third phenomenon, the cascade effect, is a chain of events caused by a disturbance that affects the land system as a result of the substitution of areas for the production of other crops in specific agro-ecological conditions or land conversion, thus leading to additional environmental effects that are not immediately measurable.
The first phenomenon, displacement, may be associated with the quinoa boom given the speed of the increase in production in Peru to meet global demand, resulting in the expansion of the acreage in traditional producing regions as well as in the expansion in the Peruvian coastal regions, where quinoa has been introduced thanks to its adaptability and tolerance of extreme environments, such as saline soils and temperatures of up to 38° C (INIA and FAO, 2015).
As can be seen in Figure 4 and Table 1, in view of the quinoa boom, the Arequipa region stands out because it largely meets demand, with an average expansion rate of 123%
in the acreage between 2008 and 2014, but the magnitude of its importance is even greater if we consider this means an increase of 7773% in the acreage, compared to the year 1995.
This extraordinary expansion meant that while in 2008 it accounted for approximately 1%, of national production, by 2014 that figure was 30%.
Table 1. Change in the expansion rate of the acreage, yield and market share, by period, in traditional quinoa farming regions in Peru
Production volume in 2014 (MT)
Region Variation in acreage (%)
Yield Kg/ha Variation %
Share of national production (%) 2002-2008 2009-2013 2013-2014 2002-2008 2009-2013 2013-2014 2002-2008 2009-2013 2013-2014
Peru (Total) 1.91 7.33 51.85 1041.00
1.51
1158.40 0.09
1681.00 44.66
100 100 100
> de 30000
Puno 0.89 3.50 7.95 1064.71
1.78
1137.20 -4.66
1121.00 14.25
81.2 72.2 31.6
Arequipa -0.71 55.31 481.29 1317.00
4.37
2379.60 24.57
4086.00 7.03
0.9 3.9 28.9
10000-30000
Junin -2.69 21.27 146.89 1291.86
-0.25
1424.00 7.70
1998.00 10.94
4.1 4.5 9.2
Ayacucho 13.39 32.03 65.40 873.00
1.96
962.00 6.23
1341.00 26.78
3.8 6.5 9.0
3000-10000
La Libertad -1.76 18.36 219.65 732.86
7.61
1214.80 13.97
1892.00 13.28
1.1 1.3 3.6
Ancash -7.00 21.33 454.55 1046.43
0.06
1063.60 4.06
1968.00 68.18
1.0 0.4 2.8
Cuzco 19.01 4.60 9.45 917.14
3.33
1009.00 4.67
1149.00 -2.04
3.4 4.9 2.6
1000-3000
Apurimac 9.33 11.80 37.20 885.00
1.25
1202.00 10.38
1339.00 4.33
2.4 3.4 2.5
Huanuco -2.95 3.97 193.87 807.86
0.68
847.80 2.81
929.00 1.17
1.0 1.0 0.7
< de 1000
Huancavelica 34.16 11.71 18.63 567.71
6.68
883.20 2.46
950.00 1.10
0.4 1.1 0.7
Cajamarca 6.04 4.63 69.26 842.43
7.56
954.80 -1.88
1022.00 8.05
0.4 0.4 0.3
Source: Elaborated by the authors based on data from the OEEE/MINAGRI (2015).
To achieve the record quinoa harvest, between 2013 and 2014 the rate of expansion of the acreage accelerated in all the traditional producing regions (Figures 1 and 4), ranging between 8% in Puno and 481% in Arequipa. Thus, there was also a redistribution of production between regions, which is highlighted by the reduction in Puno’s share of the total national production from 81% in the period 2002-2008 to 32% in 2014.
Given the situation outlined above, it is clear that the quinoa boom has caused two phenomena. On the one hand, it encouraged the acceleration of the expansion of agricultural areas in the traditional quinoa farming regions, leading to competition for greater market share. On the other hand, it led to the extension of quinoa farming to new regions. Hence, given that, worldwide, the ability to produce food is affected by the intensification of competition for land (Godfray et al., 2010), the future requirements for farmland to produce quinoa in Peru need to be taken into account by decision makers and the formulators of public policy. This is particularly important if one considers that to meet the Peruvian Ministry of Agriculture’s projected production of 212 thousand tons for the year 2020, a total of approximately 114,000 hectares of land (MINAGRI, 2015) would be required, which is 167%
more than the amount used in 2014.
The second phenomenon, the rebound effect, is related to changes in land-use in Peru, with increasingly efficient production through the use of technology and the increased number of companies related to the processing of quinoa. To highlight the first case, Table 1 shows the variation in yield per region and shows that, during the 2009-2013 period, the rate varied between -5% in Puno and 25% in Arequipa. In absolute values, this means a yield equal to 1137 kg/ha, in the case of Puno and 2380 kg/ha, in the case of Arequipa. With the increase in production between 2013 and 2014, the yield in the Puno region remained close to 1121 kg/ha, while the Arequipa region achieved a yield of 4086 kg/ha. In general, a large part of these differences can be attributed to the heterogeneity of the edaphoclimatic factors (soil and climate), at the different ecological levels where quinoa is farmed.
For example, in Puno - the main quinoa producing region – the crop occupies areas located between 3800 and 3950 meters above sea level, and resists dry and cold weather (INIA and FAO, 2015). In that region, crop rotation (potatoes, cereals, legumes, tubers, forage) is the basis of quinoa farming, with the soil left fallow to recover fertility (Mujica, Angel et al., 2001). Given the characteristics of the production system, if there is an increase in yield in this region, there may be greater demand to expand the planted area, substitute crops or abandon traditional agricultural practices in order to meet market demand. In turn, in
the case of the Arequipa region – the second largest quinoa producing region in the country since 2013 - yields are highly favorable since the weather conditions are less severe than in the Puno region and also because it includes part of the Peruvian coast. In this case, the highly efficient quinoa production also leads to an expansion rather than a reduction in the acreage, since the high yields obtained in the region make it more attractive and conducive to the expansion of activity.
To highlight another rebound effect, the case of the new quinoa producing regions along the Peruvian coast (Lambayeque, Tacna, Lima, Ica and Piura) (see Figure 1), which have higher yields than the traditional producing regions, is explored. While the yield in the Peruvian coastal regions was around 2465 kg/ha in 2014, that of the traditional producing regions was approximately 1618 Kg/ha, in the same year. So, it seems that the rebound effect was caused by technological improvements that raised the efficiency of quinoa production to meet the demand of new industrial companies that settled in the area in order to boost the integration of this product, with some added value, in global value chains. For example, in 2015, export-oriented processing plants producing ready-to eat products made from quinoa were opened in the La Libertad region, (e.g. Danper S.A.C Trujillo, Sociedad Agrícola Virú S.A.).
Land-use change through the rebound effect is especially important considering that the market for quinoa is expanding. Thus, given the expansion of the quinoa market and due to the rebound effect, and also considering that the expansion in acreage cannot continue indefinitely, there will certainly be pressure on land-use. Thus, it is crucial that decision- makers and/or public policy makers in Peru stimulate the use of good farming practices (GFP) in the production of quinoa, especially in the traditional quinoa producing regions.
The third phenomenon, the cascade effects, may be one of the consequences of the quinoa boom, since, if the growing demand for the product persists together with the need to intensify or expand production, there may be environmental impacts on the soil leading to further degradation and even desertification in certain regions (MINAM, 2011b). It is known that the soil ecosystem, through soil retention and soil formation functions, helps preserve land arable, prevent erosion, ensure productivity and protect naturally productive soils, among others (De Groot et al., 2002), thus, directly impacting the food production capacity (Moonen and Barberi, 2008).
Erosion is the main cause of land degradation in the Peruvian Sierra, affecting about 50-60% of the agricultural area under cultivation (MINAM, 2011a), within which lie the
traditional quinoa producing regions. It has already been pointed out that the future of global agricultural productivity is linked to soil erosion, and soil quality is affected by agricultural practices (Dale and Polasky, 2007). Thus, it is opportune to review the subject of soil conservation in the traditional quinoa producing regions (e.g. proper soil management, farming practices, etc.). For example, in the Puno region, there are signs traditional agricultural practices are being abandoned in order to increase the volume of quinoa produced (Soto et al., 2012). The region has the greatest genetic diversity of quinoa (Mujica et al., 2001; FAO and CIRAD, 2015; INIA and FAO, 2015) and is the home of other Andean food products of great importance (such as cañigua, mashua, oca, tarhui, etc.). This could trigger the loss of genetic diversity in the local agriculture, if other crops are no longer grown or fewer varieties are grown due to commercial pressures. Another possible consequence might be the emergence of diffficult-to-control pests due to the reduced genetic diversity and climate change in the producing regions (Swift et al., 2004; Hooper et al., 2005), reducing the number of natural enemies of pests (Landis et al., 2000), and certainly impacting on nutritional security in those regions (Kaput et al., 2015).
The conservation of agricultural biodiversity encompasses multiple dimensions:
ecosystem services (Wood et al., 2015), sustainable production, food security, product diversification, reduced dependence on external inputs, maximizing effective use of resources and improvement of livelihoods for small farmers (Jackson et al., 2007, CBD, 2013a, 2013b).
In the case of the Andean region, the biodiversity is also the basis of food sovereignty because there are human communities that maintain and support the agrobiodiversity as part of their social and natural heritage (COPISA, 2012).
Salinization is the main cause of land degradation along the Peruvian coast, which includes the new quinoa producing regions, and affects 40% of the occupied agricultural area with a significant part in the Piura and Lambayeque regions (MINAM, 2011a). Unlike the traditional producing regions, the farming practices used in the cultivation of quinoa in the new regions along the Peruvian coast are still experimental, since quinoa was introduced to reconvert areas of other crops, mainly rice.
Since 2014, the Peruvian Ministry of Agriculture has encouraged the conversion of rice farmland to quinoa in the regions of La Libertad, Lambayeque and Piura, in order to reduce the water consumption required for rice cultivation, which has accentuated the process of salinization. While rice requires, on average, 15,000 m3/ha of water, quinoa needs only 6000 m3/ha. However, while the crop conversion measures in these areas may be convenient,
phytosanitary problems affecting quinoa production could be a source of concern for local agriculture. The study by Danielsen et al. (2003) shows there are a variety of known diseases which appear especially when quinoa is grown in areas outside the traditional growing regions, although Downy Mildew (caused by the fungus Peronospora farinosa) is the most common disease in quinoa. In view of this, the ‘Import Refusal Report’ from the United States Food and Drug Administration (FDA), shows that in 2014, quinoa from Peru was refused entry due to excessive levels of pesticide residues (FDA, 2016). As it is basically dealing with quinoa from the Peruvian coast, it can be deduced that due to biological pressure from pests like ‘Ticona’ (Feltia experta) and/or ‘Kcona Kcona’ (Eurysacca quinoae Povolny) (INIA and FAO, 2015), producers adopt harmful control measures, such as the excessive use of pesticides. Furthermore, pesticides can contaminate the soil and affect its fertility, because the heavy application of pesticides can cause the decline of beneficial microorganisms in the soil (Swift et al., 2004; Aktar et al., 2009).
As shown above, through the cascade effects, the land-use changes accompanying the boom quinoa are associated with the disturbance of the soil ecosystem. Thus, the variability of expansion rate of the acreage of quinoa at the regional level requires urgent attention, mainly because soil properties are so variable over space and time (Dale and Polasky, 2007). There is a need to determine whether expanding the acreage of quinoa jeopardizes agricultural productivity, the production of other crops, and/or accelerates soil fertility loss, either by transformations to traditional agricultural practices or the excess use of pesticides (Tilman et al., 2002; Swift et al., 2004).
4. CONCLUDING REMARKS
Across the data set, it was possible to identify that the quinoa boom in Peru began in 2009, but the year 2014 marked the beginning of an unprecedented period in the history of the production of this crop. Because of the quinoa boom, the average rate of expansion of the acreage of quinoa was 16.23%. Whereas, if that boom had not occurred, the expansion rate would have been 2.76%, and in 2014, there would have been 43% fewer hectares planted with quinoa.
The quinoa boom produced two phenomena in Peru. On the one hand, it encouraged the accelerated expansion of the acreage of quinoa in traditional quinoa producing locations.
On the other hand, it led to the extension of quinoa farming to new regions. The growth of the international quinoa market from 2008 has started to show its impact on the land-use change in Peru: i) displacement (activities of migration from one place to another in such a way as to bring about land-use changes in new locations ), ii) rebound (land-use changes involving measures taken to increase the yield/productive and an increase in the number of companies operating in the sector), and iii) cascade effects (disturbances affecting the land system resulting from the substitution of areas destined for the production of other crops in specific agro-ecological conditions).
By providing a perspective on the link between the intensification of the international quinoa trade and the expansion of the acreage of quinoa in Peru, this study contributes to the literature in construction regarding the consequences of land-use changes on the sustainability of agrobiological systems. Understanding the processes of direct or indirect land-use change resulting from trade is essential so that decision-makers in Peru can better manage natural systems, in order to ensure the long-term sustainability of agricultural production in the country.
Acknowledgements
The authors are grateful to the Brazilian National Council for Research and Development – CNPq (Grant Number: CNPq-306375/2012-5) and CAPES PEC-PG (Grant number: 5898-11- 0) for their financial support.
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